We use a machine-learning approach known as boosted regression trees (BRT) to
reexamine the usefulness of selected leading indicators for predicting recessions. We …

Sectoral corporate credit risk interlinkages constitute a highly topical issue for the systemic
risk considerations of policymakers and market practitioners. We reveal the macroeconomic …

In this paper, we develop and apply novel machine learning and statistical methods to
analyse the determinants of students' PISA 2015 test scores in nine countries: Australia …

The paper proposes a multivariate asymmetric stochastic volatility approach, allowing for
common factors that detect and measure herding behavior conditional on the stylized facts …

We use a machine‐learning algorithm known as boosted regression trees (BRT) to
implement an orthogonality test of the rationality of aggregate stock market forecasts. The …

We analyse systemic risk in the core global banking system using a new network-based
spectral eigen-pair method, which treats network failure as a dynamical system stability …

Investors are constantly searching for methods to generate value above passive investment
techniques. Therefore, analysing the performance of hedge funds as compared to mutual …

This research aims at providing an empirical evidence for the otherwise well described
phenomena of robust-yet-fragile. The theory suggest that the connectedness of the financial …

In April 2010 Europe was shocked by the Greek financial turmoil. At that time, the global
financial crisis, which started in the summer of 2007 and reached systemic dimensions in …

We use a machine-learning approach known as Boosted Regression Trees (BRT) to
reexamine the usefulness of selected leading indicators for predicting recessions. We …